Degree Days

Degree Days

Weather Data for Energy Saving

Why Get Your Degree Days from Us?

Degree days are degree days, right? Does it really matter where you get them from?

Well, yes it does! We don't like to brag, but we feel it's important for newcomers to energy data analysis to understand that there are compelling reasons why 5000+ energy professionals regularly download data from Degree, and why our API is used by numerous energy software systems worldwide:

1. We calculate degree days properly

We don't use a crude approximation method based on daily average temperatures. We calculate degree days properly from detailed temperature records (typically hourly or better).

It's a lot more work for us, but it's worth it to provide accurate data. Hot days and cold nights don't average out in energy consumption, and they shouldn't average out in degree days either. (See the bottom of this page for a more detailed explanation of this.)

2. Data in any base temperature

Buildings vary significantly in terms of their thermal properties and usage, and consequently their base temperatures. With Degree you can stick with the "defaults" (e.g. 65°F, 18°C, 15.5°C), or, for improved accuracy, you can use the real base temperatures of the buildings you analyze.

3. Convenient breakdown options

You usually want degree days to match your metered periods of energy consumption. Degree covers common use cases by offering daily, weekly, and monthly data (as well as multi-year averages). And for anything else you can copy/paste in custom dates from Excel and our system will generate degree days to match.

4. Sensible handling of real-world data problems

A simple star rating to indicate station data quality

Virtually all real-world weather stations have some missing or erroneous temperature readings. Even the highest-quality stations have problems occasionally.

Degree has been carefully programmed to handle these problems. It fills gaps and periods of erroneous reporting with estimates, using "% estimated" figures and star ratings to quantify the extent of the estimation and to help you favour the weather stations with better data. Most importantly, Degree will only ever provide continuous sets of usable data. That means no gaps, and none of the cryptic special values like "−1", "9999", or "N/A" that you will often find in data from other systems.

5. Worldwide coverage and calculation consistency

Degree provides degree days for locations worldwide, using the same accurate calculation processes for all of them. Using our system, degree days from country A are directly comparable with degree days from country B. This is not the case if you're getting degree days from multiple different sources, each using different approximation methods.

6. Better regression analysis

Regression is key to using degree days effectively, as our articles explain. You can do simple regression in Excel, but our regression tool goes much further, running thousands of regressions against your energy data to find the best statistical fit, testing HDD and CDD separately (simple regression) and together (multiple regression), in a wide range of base temperatures.

This makes it much easier for you to find the best regression models (with the best base temperatures) and get more accurate results from your energy data analysis.

7. Bulk data access for multi-site analysis and automated software systems

Data is available from our website as simple spreadsheet downloads, but we also provide a desktop app that makes it easy to download degree days for lots of locations at once. It's important for many energy managers in multi-site organizations and energy professionals who manage energy usage for a large number of clients. And we have an API that enables software to fetch data from Degree automatically and is a key part of many energy-management software systems worldwide.

8. We're widely used, and popular!

Thousands of energy professionals use Degree regularly, and it is also used by larger organizations such as big multinationals, universities, and government bodies, many of whom use our products to facilitate their larger-scale data analysis. And through our API we supply data to numerous energy-software providers who, in turn, feed it into the energy data analysis and reporting that they provide to all of their users.

The bottom line is that the data from our system is helping to drive energy saving in millions of buildings around the world. Which naturally makes us very pleased! As do the kind words we often receive from people and organizations that depend on us. If you are interested, you can read a few quotes.

The extensive worldwide usage of our data also brings a level of collective scrutiny that would make it very difficult for any significant errors to exist undetected in our data-calculation processes. This is not a substitute for the thorough internal testing and monitoring systems we have in place, but it does provide additional reassurance of our data quality.

More on calculating degree days properly

Point 1 above deserves further explanation.

Degree days are defined as the integral of the differences between the outside air temperature and the base temperature, over time. If the outside air temperature drops below the heating base temperature, the building needs heating and the heating degree days accumulate; if it rises above the cooling base temperature, the building needs cooling and the cooling degree days accumulate.

An example chart helps to make this clearer:

Calculating cooling degree days

The outside air temperature varies a lot, so to calculate degree days accurately requires detailed temperature records like these. However:

Many sources still calculate degree days approximately, from daily average/maximum/minimum temperatures

Detailed temperature records haven't always been available. And even when they were available, in the days before computers it wasn't really feasible to turn them into accurate degree days on a large scale. So people made do with approximation methods based on daily average/maximum/minimum temperatures. The simplest of these was the Mean Temperature Method e.g. HDD = 65 − ((max + min) / 2), CDD = ((max + min) / 2) − 65.

The Mean Temperature Method was an acceptable compromise in its day, but by modern standards it simply isn't good enough for accurate energy data analysis. Its shortcomings are easy to understand with an example:

Consider a building with a cooling base temperature of 65°F. If the outside air temperature rises above 65°F, the building needs cooling to stop it getting too hot. Now consider a common climate: hot days and cooler nights. In the day the temperature rises to around 85°F; at night it drops to around 45°F. (max + min) / 2 gives us an approximate average temperature of 65°F, so, according to the Mean Temperature Method, the cooling degree days are zero and the building supposedly needs no cooling...

Yes, it's pretty obvious that's not the case. Or at least it is to anyone with a building, a thermometer, and a vague awareness of their HVAC system. Hot days and cold nights don't cancel each other out, same as hot summers and cold winters don't cancel each other out. Real-world thermostatically-controlled buildings simply don't work like that.

Unfortunately the Mean Temperature Method assumes that they do. And, despite the fact that detailed temperature readings and powerful computers to process them are now both readily available, many weather-data providers are still using that outdated approximation method. Either because it's the easiest option for them, or because they simply don't know any better.

There are other approximation methods too. Many assume a set pattern of temperature variation between daily maximum and minimum values, like a sine-wave pattern, for example. This can improve accuracy to some extent, but of course temperatures don't follow a set daily pattern in the real world. The only way to know what the temperature really does is to measure it, frequently, and weather stations have been doing this for a long time. So it's harder to justify using these approximation methods nowadays.

We calculate degree days accurately, from detailed temperature readings taken throughout each day

Degree calculates degree days accurately, taking into account the temperature variations that occur within each day. We use the full detail of the temperature records available: 60-minute readings, 30-minute readings, 20-minute readings, and more. Whatever the weather station records, we use.

Accurate calculation like this is always preferable to crude estimation, but it makes more of a difference on some days than others. On days when the outside air temperature stays either above or below the base temperature, the difference is usually small. But the advantage of accurate calculation is much more apparent on days when the outside air temperature crosses the base temperature (i.e. going both above and below), like in the examples above. Such days are common in many climates, so it's important to account for them properly.

Our calculation process is many times more complicated and computationally expensive than it would be if we'd settled for a simple approximation method. But, as a specialist provider of degree days, we feel it's important to calculate the data as accurately as we can. And data from Degree is a better indicator of real-world energy consumption as a result.

(If you're interested in much more detail on this, please see our page on calculating degree days.)

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